InĀ [7]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
from datetime import datetime
InĀ [8]:
covid_df = pd.read_csv("C:\\Users\\KHUSHI\\Downloads\\Covid-19-Data-Analysis\\covid_19_india.csv")
InĀ [9]:
covid_df.head(10)
Out[9]:
| Sno | Date | Time | State/UnionTerritory | ConfirmedIndianNational | ConfirmedForeignNational | Cured | Deaths | Confirmed | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 2020-01-30 | 6:00 PM | Kerala | 1 | 0 | 0.0 | 0.0 | 1.0 |
| 1 | 2.0 | 2020-01-31 | 6:00 PM | Kerala | 1 | 0 | 0.0 | 0.0 | 1.0 |
| 2 | 3.0 | 2020-02-01 | 6:00 PM | Kerala | 2 | 0 | 0.0 | 0.0 | 2.0 |
| 3 | 4.0 | 2020-02-02 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
| 4 | 5.0 | 2020-02-03 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
| 5 | 6.0 | 2020-02-04 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
| 6 | 7.0 | 2020-02-05 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
| 7 | 8.0 | 2020-02-06 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
| 8 | 9.0 | 2020-02-07 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
| 9 | 10.0 | 2020-02-08 | 6:00 PM | Kerala | 3 | 0 | 0.0 | 0.0 | 3.0 |
InĀ [13]:
covid_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15114 entries, 0 to 15113 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Sno 15086 non-null float64 1 Date 15086 non-null object 2 Time 15086 non-null object 3 State/UnionTerritory 15086 non-null object 4 ConfirmedIndianNational 15086 non-null object 5 ConfirmedForeignNational 15086 non-null object 6 Cured 15086 non-null float64 7 Deaths 15086 non-null float64 8 Confirmed 15086 non-null float64 dtypes: float64(4), object(5) memory usage: 1.0+ MB
InĀ [15]:
covid_df.describe()
Out[15]:
| Sno | Cured | Deaths | Confirmed | |
|---|---|---|---|---|
| count | 15086.000000 | 1.508600e+04 | 15086.000000 | 1.508600e+04 |
| mean | 7543.500000 | 1.747937e+05 | 2721.084449 | 1.942820e+05 |
| std | 4355.097416 | 3.648330e+05 | 7182.672358 | 4.095184e+05 |
| min | 1.000000 | 0.000000e+00 | 0.000000 | 0.000000e+00 |
| 25% | 3772.250000 | 1.685000e+03 | 12.000000 | 2.935500e+03 |
| 50% | 7543.500000 | 1.964700e+04 | 364.000000 | 2.608150e+04 |
| 75% | 11314.750000 | 2.087552e+05 | 2170.000000 | 2.216012e+05 |
| max | 15086.000000 | 4.927480e+06 | 83777.000000 | 5.433506e+06 |
InĀ [17]:
vaccine_df = pd.read_csv("C:\\Users\\KHUSHI\\Downloads\\Covid-19-Data-Analysis\\covid_vaccine_statewise.csv")
InĀ [19]:
vaccine_df.head()
Out[19]:
| Updated On | State | Total Doses Administered | Sessions | Sites | First Dose Administered | Second Dose Administered | Male (Doses Administered) | Female (Doses Administered) | Transgender (Doses Administered) | ... | 18-44 Years (Doses Administered) | 45-60 Years (Doses Administered) | 60+ Years (Doses Administered) | 18-44 Years(Individuals Vaccinated) | 45-60 Years(Individuals Vaccinated) | 60+ Years(Individuals Vaccinated) | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Individuals Vaccinated | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 23757.0 | 24517.0 | 2.0 | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 27348.0 | 31252.0 | 4.0 | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 41361.0 | 58083.0 | 5.0 | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 81901.0 | 113613.0 | 11.0 | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 98111.0 | 153145.0 | 24.0 | 251280.0 |
5 rows Ć 24 columns
InĀ [21]:
covid_df.drop(["Sno","Time" , "ConfirmedIndianNational", "ConfirmedForeignNational"], inplace = True, axis = 1)
InĀ [23]:
covid_df.head()
Out[23]:
| Date | State/UnionTerritory | Cured | Deaths | Confirmed | |
|---|---|---|---|---|---|
| 0 | 2020-01-30 | Kerala | 0.0 | 0.0 | 1.0 |
| 1 | 2020-01-31 | Kerala | 0.0 | 0.0 | 1.0 |
| 2 | 2020-02-01 | Kerala | 0.0 | 0.0 | 2.0 |
| 3 | 2020-02-02 | Kerala | 0.0 | 0.0 | 3.0 |
| 4 | 2020-02-03 | Kerala | 0.0 | 0.0 | 3.0 |
InĀ [25]:
covid_df['Date'] = pd.to_datetime(covid_df['Date'], format = '%Y-%m-%d')
InĀ [27]:
covid_df.head()
Out[27]:
| Date | State/UnionTerritory | Cured | Deaths | Confirmed | |
|---|---|---|---|---|---|
| 0 | 2020-01-30 | Kerala | 0.0 | 0.0 | 1.0 |
| 1 | 2020-01-31 | Kerala | 0.0 | 0.0 | 1.0 |
| 2 | 2020-02-01 | Kerala | 0.0 | 0.0 | 2.0 |
| 3 | 2020-02-02 | Kerala | 0.0 | 0.0 | 3.0 |
| 4 | 2020-02-03 | Kerala | 0.0 | 0.0 | 3.0 |
InĀ [29]:
#Active cases
covid_df['Active_Cases'] = covid_df['Confirmed'] - (covid_df['Cured'] + covid_df['Deaths'])
covid_df.tail()
Out[29]:
| Date | State/UnionTerritory | Cured | Deaths | Confirmed | Active_Cases | |
|---|---|---|---|---|---|---|
| 15109 | NaT | NaN | NaN | NaN | NaN | NaN |
| 15110 | NaT | NaN | NaN | NaN | NaN | NaN |
| 15111 | NaT | NaN | NaN | NaN | NaN | NaN |
| 15112 | NaT | NaN | NaN | NaN | NaN | NaN |
| 15113 | NaT | NaN | NaN | NaN | NaN | NaN |
InĀ [31]:
statewise = pd.pivot_table(covid_df,values = ['Confirmed', 'Deaths', 'Cured'], index = 'State/UnionTerritory', aggfunc = 'max')
InĀ [33]:
statewise['Recovery Rate'] = statewise['Cured']*100/statewise['Confirmed']
InĀ [35]:
statewise['Mortality Rate'] = statewise['Deaths']*100/statewise['Confirmed']
InĀ [37]:
statewise = statewise.sort_values(by = 'Confirmed', ascending = False)
InĀ [39]:
statewise.style.background_gradient(cmap = 'cubehelix')
Out[39]:
| Ā | Confirmed | Cured | Deaths | Recovery Rate | Mortality Rate |
|---|---|---|---|---|---|
| State/UnionTerritory | Ā | Ā | Ā | Ā | Ā |
| Maharashtra | 5433506.000000 | 4927480.000000 | 83777.000000 | 90.686934 | 1.541859 |
| Karnataka | 2272374.000000 | 1674487.000000 | 22838.000000 | 73.688882 | 1.005028 |
| Kerala | 2200706.000000 | 1846105.000000 | 6612.000000 | 83.886944 | 0.300449 |
| Tamil Nadu | 1664350.000000 | 1403052.000000 | 18369.000000 | 84.300297 | 1.103674 |
| Uttar Pradesh | 1637663.000000 | 1483249.000000 | 18072.000000 | 90.571076 | 1.103524 |
| Andhra Pradesh | 1475372.000000 | 1254291.000000 | 9580.000000 | 85.015237 | 0.649328 |
| Delhi | 1402873.000000 | 1329899.000000 | 22111.000000 | 94.798246 | 1.576123 |
| West Bengal | 1171861.000000 | 1026492.000000 | 13576.000000 | 87.595030 | 1.158499 |
| Chhattisgarh | 925531.000000 | 823113.000000 | 12036.000000 | 88.934136 | 1.300443 |
| Rajasthan | 879664.000000 | 713129.000000 | 7080.000000 | 81.068340 | 0.804853 |
| Gujarat | 766201.000000 | 660489.000000 | 9269.000000 | 86.203098 | 1.209735 |
| Madhya Pradesh | 742718.000000 | 652612.000000 | 7139.000000 | 87.868074 | 0.961199 |
| Haryana | 709689.000000 | 626852.000000 | 6923.000000 | 88.327704 | 0.975498 |
| Bihar | 664115.000000 | 595377.000000 | 4039.000000 | 89.649684 | 0.608178 |
| Odisha | 633302.000000 | 536595.000000 | 2357.000000 | 84.729718 | 0.372176 |
| Telangana | 536766.000000 | 485644.000000 | 3012.000000 | 90.475924 | 0.561138 |
| Punjab | 511652.000000 | 427058.000000 | 12317.000000 | 83.466497 | 2.407300 |
| Telengana | 443360.000000 | 362160.000000 | 2312.000000 | 81.685312 | 0.521472 |
| Assam | 340858.000000 | 290774.000000 | 2344.000000 | 85.306491 | 0.687676 |
| Jharkhand | 320934.000000 | 284805.000000 | 4601.000000 | 88.742545 | 1.433628 |
| Uttarakhand | 295790.000000 | 214426.000000 | 5132.000000 | 72.492647 | 1.735015 |
| Jammu and Kashmir | 251919.000000 | 197701.000000 | 3293.000000 | 78.478003 | 1.307166 |
| Himachal Pradesh | 166678.000000 | 129330.000000 | 2460.000000 | 77.592724 | 1.475900 |
| Goa | 138776.000000 | 112633.000000 | 2197.000000 | 81.161728 | 1.583127 |
| Puducherry | 87749.000000 | 69060.000000 | 1212.000000 | 78.701752 | 1.381212 |
| Chandigarh | 56513.000000 | 48831.000000 | 647.000000 | 86.406667 | 1.144869 |
| Tripura | 42776.000000 | 36402.000000 | 450.000000 | 85.099121 | 1.051992 |
| Manipur | 40683.000000 | 33466.000000 | 612.000000 | 82.260404 | 1.504314 |
| Meghalaya | 24872.000000 | 19185.000000 | 355.000000 | 77.134931 | 1.427308 |
| Arunachal Pradesh | 22462.000000 | 19977.000000 | 88.000000 | 88.936871 | 0.391773 |
| Nagaland | 18714.000000 | 14079.000000 | 228.000000 | 75.232446 | 1.218339 |
| Ladakh | 16784.000000 | 15031.000000 | 170.000000 | 89.555529 | 1.012869 |
| Sikkim | 11689.000000 | 8427.000000 | 212.000000 | 72.093421 | 1.813671 |
| Dadra and Nagar Haveli and Daman and Diu | 9652.000000 | 8944.000000 | 4.000000 | 92.664733 | 0.041442 |
| Cases being reassigned to states | 9265.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Mizoram | 9252.000000 | 7094.000000 | 29.000000 | 76.675313 | 0.313446 |
| Andaman and Nicobar Islands | 6674.000000 | 6359.000000 | 92.000000 | 95.280192 | 1.378484 |
| Lakshadweep | 5212.000000 | 3915.000000 | 15.000000 | 75.115119 | 0.287797 |
| Unassigned | 77.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Daman & Diu | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
InĀ [41]:
#Top 10 active cases states
top_10_active_cases = covid_df.groupby(by = 'State/UnionTerritory').max()[['Active_Cases','Date']].sort_values(by = ['Active_Cases'],ascending = False).reset_index()
InĀ [43]:
fig = plt.figure(figsize=(16,9))
<Figure size 1600x900 with 0 Axes>
InĀ [45]:
plt.title('Top 10 states with most active cases in India', size = 25)
Out[45]:
Text(0.5, 1.0, 'Top 10 states with most active cases in India')
InĀ [47]:
ax = sns.barplot(data = top_10_active_cases.iloc[:10], y = 'Active_Cases', x = 'State/UnionTerritory', linewidth = 2, edgecolor ='black')
InĀ [49]:
#Top 10 active cases states
top_10_active_cases = covid_df.groupby(by = 'State/UnionTerritory').max()[['Active_Cases','Date']].sort_values(by = ['Active_Cases'],ascending = False).reset_index()
fig = plt.figure(figsize=(16,9))
plt.title('Top 10 states with most active cases in India', size = 25)
ax = sns.barplot(data = top_10_active_cases.iloc[:10], y = 'Active_Cases', x = 'State/UnionTerritory', linewidth = 2, edgecolor ='black')
plt.xlabel('States')
plt.ylabel('Total Active Cases')
plt.show()
InĀ [51]:
# Top states with highest deaths
top_10_deaths = covid_df.groupby(by = 'State/UnionTerritory').max()[['Deaths','Date']].sort_values(by = ['Deaths'], ascending = False).reset_index()
fig = plt.figure(figsize=(18,5))
plt.title('Top 10 states with most Deaths', size = 25)
ax = sns.barplot(data = top_10_deaths.iloc[:12], y = 'Deaths' , x = 'State/UnionTerritory',linewidth = 2, edgecolor = 'black')
plt.xlabel('States')
plt.ylabel('Total Death Cases')
plt.show()
InĀ [55]:
# Growth trend
#ERROR AARHA ISME
fig = plt.figure(figsize = (12,6))
ax = sns.lineplot(data = covid_df[covid_df['State/UnionTerritory'].isin(['Maharashtra','Karnataka','Kerala','Tamil Nadu','Uttar Pradesh'])],
x = 'Date', y ='Active_Cases' , hue = 'State/UnionTerritory')
ax.set_title('Top 5 Affected States in India', size = 16)
Out[55]:
Text(0.5, 1.0, 'Top 5 Affected States in India')
InĀ [63]:
vaccine_df.head()
Out[63]:
| Updated On | State | Total Doses Administered | Sessions | Sites | First Dose Administered | Second Dose Administered | Male (Doses Administered) | Female (Doses Administered) | Transgender (Doses Administered) | ... | 18-44 Years (Doses Administered) | 45-60 Years (Doses Administered) | 60+ Years (Doses Administered) | 18-44 Years(Individuals Vaccinated) | 45-60 Years(Individuals Vaccinated) | 60+ Years(Individuals Vaccinated) | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Individuals Vaccinated | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 23757.0 | 24517.0 | 2.0 | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 27348.0 | 31252.0 | 4.0 | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 41361.0 | 58083.0 | 5.0 | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 81901.0 | 113613.0 | 11.0 | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 98111.0 | 153145.0 | 24.0 | 251280.0 |
5 rows Ć 24 columns
InĀ [65]:
vaccine_df.rename(columns = {'Updated On' : 'Vaccine_Date'}, inplace = True)
InĀ [67]:
vaccine_df.head(10)
Out[67]:
| Vaccine_Date | State | Total Doses Administered | Sessions | Sites | First Dose Administered | Second Dose Administered | Male (Doses Administered) | Female (Doses Administered) | Transgender (Doses Administered) | ... | 18-44 Years (Doses Administered) | 45-60 Years (Doses Administered) | 60+ Years (Doses Administered) | 18-44 Years(Individuals Vaccinated) | 45-60 Years(Individuals Vaccinated) | 60+ Years(Individuals Vaccinated) | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Individuals Vaccinated | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 23757.0 | 24517.0 | 2.0 | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 27348.0 | 31252.0 | 4.0 | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 41361.0 | 58083.0 | 5.0 | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 81901.0 | 113613.0 | 11.0 | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 98111.0 | 153145.0 | 24.0 | 251280.0 |
| 5 | 21/01/2021 | India | 365965.0 | 32226.0 | 12600.0 | 365965.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 132784.0 | 233143.0 | 38.0 | 365965.0 |
| 6 | 22/01/2021 | India | 549381.0 | 36988.0 | 14115.0 | 549381.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 193899.0 | 355402.0 | 80.0 | 549381.0 |
| 7 | 23/01/2021 | India | 759008.0 | 43076.0 | 15605.0 | 759008.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 267856.0 | 491049.0 | 103.0 | 759008.0 |
| 8 | 24/01/2021 | India | 835058.0 | 49851.0 | 18111.0 | 835058.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 296283.0 | 538647.0 | 128.0 | 835058.0 |
| 9 | 25/01/2021 | India | 1277104.0 | 55151.0 | 19682.0 | 1277104.0 | 0.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | 444137.0 | 832766.0 | 201.0 | 1277104.0 |
10 rows Ć 24 columns
InĀ [69]:
vaccine_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 7845 entries, 0 to 7844 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Vaccine_Date 7845 non-null object 1 State 7845 non-null object 2 Total Doses Administered 7621 non-null float64 3 Sessions 7621 non-null float64 4 Sites 7621 non-null float64 5 First Dose Administered 7621 non-null float64 6 Second Dose Administered 7621 non-null float64 7 Male (Doses Administered) 7461 non-null float64 8 Female (Doses Administered) 7461 non-null float64 9 Transgender (Doses Administered) 7461 non-null float64 10 Covaxin (Doses Administered) 7621 non-null float64 11 CoviShield (Doses Administered) 7621 non-null float64 12 Sputnik V (Doses Administered) 2995 non-null float64 13 AEFI 5438 non-null float64 14 18-44 Years (Doses Administered) 1702 non-null float64 15 45-60 Years (Doses Administered) 1702 non-null float64 16 60+ Years (Doses Administered) 1702 non-null float64 17 18-44 Years(Individuals Vaccinated) 3733 non-null float64 18 45-60 Years(Individuals Vaccinated) 3734 non-null float64 19 60+ Years(Individuals Vaccinated) 3734 non-null float64 20 Male(Individuals Vaccinated) 160 non-null float64 21 Female(Individuals Vaccinated) 160 non-null float64 22 Transgender(Individuals Vaccinated) 160 non-null float64 23 Total Individuals Vaccinated 5919 non-null float64 dtypes: float64(22), object(2) memory usage: 1.4+ MB
InĀ [71]:
vaccine_df.isnull().sum()
Out[71]:
Vaccine_Date 0 State 0 Total Doses Administered 224 Sessions 224 Sites 224 First Dose Administered 224 Second Dose Administered 224 Male (Doses Administered) 384 Female (Doses Administered) 384 Transgender (Doses Administered) 384 Covaxin (Doses Administered) 224 CoviShield (Doses Administered) 224 Sputnik V (Doses Administered) 4850 AEFI 2407 18-44 Years (Doses Administered) 6143 45-60 Years (Doses Administered) 6143 60+ Years (Doses Administered) 6143 18-44 Years(Individuals Vaccinated) 4112 45-60 Years(Individuals Vaccinated) 4111 60+ Years(Individuals Vaccinated) 4111 Male(Individuals Vaccinated) 7685 Female(Individuals Vaccinated) 7685 Transgender(Individuals Vaccinated) 7685 Total Individuals Vaccinated 1926 dtype: int64
InĀ [73]:
vaccination = vaccine_df.drop(columns = ['Sputnik V (Doses Administered)','AEFI','18-44 Years (Doses Administered)','60+ Years (Doses Administered)'],axis=1)
InĀ [75]:
vaccination.head()
Out[75]:
| Vaccine_Date | State | Total Doses Administered | Sessions | Sites | First Dose Administered | Second Dose Administered | Male (Doses Administered) | Female (Doses Administered) | Transgender (Doses Administered) | Covaxin (Doses Administered) | CoviShield (Doses Administered) | 45-60 Years (Doses Administered) | 18-44 Years(Individuals Vaccinated) | 45-60 Years(Individuals Vaccinated) | 60+ Years(Individuals Vaccinated) | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Individuals Vaccinated | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16/01/2021 | India | 48276.0 | 3455.0 | 2957.0 | 48276.0 | 0.0 | NaN | NaN | NaN | 579.0 | 47697.0 | NaN | NaN | NaN | NaN | 23757.0 | 24517.0 | 2.0 | 48276.0 |
| 1 | 17/01/2021 | India | 58604.0 | 8532.0 | 4954.0 | 58604.0 | 0.0 | NaN | NaN | NaN | 635.0 | 57969.0 | NaN | NaN | NaN | NaN | 27348.0 | 31252.0 | 4.0 | 58604.0 |
| 2 | 18/01/2021 | India | 99449.0 | 13611.0 | 6583.0 | 99449.0 | 0.0 | NaN | NaN | NaN | 1299.0 | 98150.0 | NaN | NaN | NaN | NaN | 41361.0 | 58083.0 | 5.0 | 99449.0 |
| 3 | 19/01/2021 | India | 195525.0 | 17855.0 | 7951.0 | 195525.0 | 0.0 | NaN | NaN | NaN | 3017.0 | 192508.0 | NaN | NaN | NaN | NaN | 81901.0 | 113613.0 | 11.0 | 195525.0 |
| 4 | 20/01/2021 | India | 251280.0 | 25472.0 | 10504.0 | 251280.0 | 0.0 | NaN | NaN | NaN | 3946.0 | 247334.0 | NaN | NaN | NaN | NaN | 98111.0 | 153145.0 | 24.0 | 251280.0 |
InĀ [77]:
# Male vs Female Vaccination
male = vaccination['Male(Individuals Vaccinated)'].sum()
female = vaccination['Female(Individuals Vaccinated)'].sum()
px.pie(names=['Male','Female'], values=[male,female], title = 'Male and Female Vaccination',color_discrete_sequence=px.colors.sequential.RdBu)
InĀ [79]:
# Remove rows where state = India
vaccine = vaccine_df[vaccine_df.State!='India']
vaccine
Out[79]:
| Vaccine_Date | State | Total Doses Administered | Sessions | Sites | First Dose Administered | Second Dose Administered | Male (Doses Administered) | Female (Doses Administered) | Transgender (Doses Administered) | ... | 18-44 Years (Doses Administered) | 45-60 Years (Doses Administered) | 60+ Years (Doses Administered) | 18-44 Years(Individuals Vaccinated) | 45-60 Years(Individuals Vaccinated) | 60+ Years(Individuals Vaccinated) | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total Individuals Vaccinated | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 212 | 16/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 213 | 17/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 214 | 18/01/2021 | Andaman and Nicobar Islands | 42.0 | 9.0 | 2.0 | 42.0 | 0.0 | 29.0 | 13.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 42.0 |
| 215 | 19/01/2021 | Andaman and Nicobar Islands | 89.0 | 12.0 | 2.0 | 89.0 | 0.0 | 53.0 | 36.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 89.0 |
| 216 | 20/01/2021 | Andaman and Nicobar Islands | 124.0 | 16.0 | 3.0 | 124.0 | 0.0 | 67.0 | 57.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 124.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 7840 | 11/08/2021 | West Bengal | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7841 | 12/08/2021 | West Bengal | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7842 | 13/08/2021 | West Bengal | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7843 | 14/08/2021 | West Bengal | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7844 | 15/08/2021 | West Bengal | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
7633 rows Ć 24 columns
InĀ [81]:
vaccine.rename(columns ={'Total Individuals Vaccinated' : 'Total'}, inplace = True)
vaccine.head()
C:\Users\KHUSHI\AppData\Local\Temp\ipykernel_10420\802833517.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[81]:
| Vaccine_Date | State | Total Doses Administered | Sessions | Sites | First Dose Administered | Second Dose Administered | Male (Doses Administered) | Female (Doses Administered) | Transgender (Doses Administered) | ... | 18-44 Years (Doses Administered) | 45-60 Years (Doses Administered) | 60+ Years (Doses Administered) | 18-44 Years(Individuals Vaccinated) | 45-60 Years(Individuals Vaccinated) | 60+ Years(Individuals Vaccinated) | Male(Individuals Vaccinated) | Female(Individuals Vaccinated) | Transgender(Individuals Vaccinated) | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 212 | 16/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 213 | 17/01/2021 | Andaman and Nicobar Islands | 23.0 | 2.0 | 2.0 | 23.0 | 0.0 | 12.0 | 11.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23.0 |
| 214 | 18/01/2021 | Andaman and Nicobar Islands | 42.0 | 9.0 | 2.0 | 42.0 | 0.0 | 29.0 | 13.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 42.0 |
| 215 | 19/01/2021 | Andaman and Nicobar Islands | 89.0 | 12.0 | 2.0 | 89.0 | 0.0 | 53.0 | 36.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 89.0 |
| 216 | 20/01/2021 | Andaman and Nicobar Islands | 124.0 | 16.0 | 3.0 | 124.0 | 0.0 | 67.0 | 57.0 | 0.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 124.0 |
5 rows Ć 24 columns
InĀ [83]:
# Most vaccinated State
max_vac = vaccine.groupby('State')['Total'].sum().to_frame('Total')
max_vac = max_vac.sort_values('Total', ascending = False)[:5]
max_vac
Out[83]:
| Total | |
|---|---|
| State | |
| Maharashtra | 1.403075e+09 |
| Uttar Pradesh | 1.200575e+09 |
| Rajasthan | 1.141163e+09 |
| Gujarat | 1.078261e+09 |
| West Bengal | 9.250227e+08 |
InĀ [85]:
fig = plt.figure(figsize=(10,5))
plt.title('Top Vaccinated States in India', size = 20)
x = sns.barplot(data = max_vac.iloc[:10],y = max_vac.Total, x = max_vac.index, linewidth=2,edgecolor = 'black',palette='rocket')
plt.xlabel('States')
plt.ylabel('Vaccination')
plt.show()
C:\Users\KHUSHI\AppData\Local\Temp\ipykernel_10420\4042747451.py:3: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
InĀ [90]:
fig = plt.figure(figsize=(10, 5))
# Set the title for the plot
plt.title('Least Vaccinated States in India', size=20)
# Sort the DataFrame to get the least vaccinated states (smallest values at the top)
min_vac = max_vac.sort_values(by='Total').iloc[:10] # Assuming 'Total' is the column for vaccination counts
# Create the bar plot
x = sns.barplot(
data=min_vac,
y='Total', # Column with the vaccination totals
x=min_vac.index, # Assuming the index contains state names
linewidth=2,
edgecolor='black',
palette='rocket'
)
# Set the labels for the x and y axes
plt.xlabel('States')
plt.ylabel('Vaccination')
C:\Users\KHUSHI\AppData\Local\Temp\ipykernel_10420\259435976.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
Out[90]:
Text(0, 0.5, 'Vaccination')